Patients with chronic kidney disease (CKD) have an increased risk for developing peripheral arterial disease (PAD). The aim of this study was to examine the cross-sectional association between novel risk factors and prevalent PAD in patients with CKD. A total of 3,758 patients with estimated glomerular filtration rates of 20 to 70 ml/min/1.73 m 2 who participated in the Chronic Renal Insufficiency Cohort (CRIC) study were included in the present analysis. PAD was defined as an ankle-brachial index <0.9 or a history of arm or leg revascularization. After adjustment for age, gender, race, cigarette smoking, physical activity, history of hypertension and diabetes, pulse pressure, high-density lipoprotein cholesterol, estimated glomerular filtration rate, and CRIC clinical sites, several novel risk factors were significantly associated with PAD. For example, odds ratios for a 1-SD higher level of risk factors were 1.18 (95% confidence interval [CI] 1.08 to 1.29) for log-transformed high-sensitivity C-reactive protein, 1.18 (95% CI 1.08 to 1.29) for white blood cell count, 1.15 (95% CI 1.05 to 1.25) for fibrinogen, 1.13 (95% CI 1.03 to 1.24) for uric acid, 1.14 (95% CI 1.02 to 1.26) for glycosylated hemoglobin, 1.11 (95% CI 1.00 to 1.23) for log-transformed homeostasis model assessment of insulin resistance, and 1.35 (95% CI 1.18 to 1.55) for cystatin C. In conclusion, these data indicate that inflammation, prothrombotic state, oxidative stress, insulin resistance, and cystatin C were associated with an increased prevalence of PAD in patients with CKD. Further studies are warranted to examine the causal effect of these risk factors on PAD in patients with CKD.
It is well established that older age, cigarette smoking, physical inactivity, hypertension, diabetes, and hypercholesterolemia play an important role in the initiation and development of atherosclerosis and its clinical manifestations, including peripheral arterial disease (PAD). However, these traditional risk factors do not entirely explain the excess risk for PAD in some subjects, such as patients with chronic kidney disease (CKD). The identification of novel risk factors for PAD may help in the development of strategies for the prevention and treatment of PAD in patients with CKD. The Chronic Renal Insufficiency Cohort (CRIC) study is a large prospective cohort study of patients with varying degrees of CKD aimed at investigating risk factors for the progression of CKD and development of cardiovascular disease. In the present analysis, we examine the association between a panel of novel cardiovascular disease risk factors and the prevalence of PAD after adjustment for established cardiovascular disease risk factors in patients with CKD.
Methods
The CRIC study participants include a racially and ethnically diverse group of men and women who were aged 21 to 74 years old and had CKD on the basis of age-based estimated glomerular filtration rate (eGFR). A total of 3,939 CRIC participants were recruited from May 2003 to August 2008 from 7 clinical centers in the United States. Participants were identified through searches of laboratory databases, medical records, and referrals from health care providers. Patients with cirrhosis, human immunodeficiency virus infection, polycystic kidney disease, or renal cell carcinoma; those on dialysis or recipients of kidney transplants; and those taking immunosuppressant drugs were excluded. In the present analysis, participants with histories of amputation and with ankle brachial indexes >0.9 (n = 89), or ankle brachial indexes >1.4 (n = 92), were excluded because of uncertainty regarding their PAD status. After exclusion, 3,758 CRIC participants were included in the analysis.
This study was approved by the institutional review board of each of the participating clinical centers and the scientific and data coordinating center. Written informed consent was obtained from all participants. This study also conformed to the guidelines of the Health Insurance Portability and Accountability Act.
All study data were collected by trained study staff members during CRIC clinical visits. All data collection procedures and equipment were standardized across study sites. A baseline medical history questionnaire was administered, in which participants were queried about their histories of PAD (claudication, amputation, or angioplasty and procedures to open up blood vessels in the arms or legs). Questionnaires also assessed demographic characteristics and lifestyle risk factors. Current smokers were defined as participants who currently smoked and had smoked >100 cigarettes in their lifetimes. Former smokers were defined as participants who had smoked >100 cigarettes in their lifetimes in the past. Alcohol drinkers were defined as participants who consumed ≥1 beverage containing alcohol each week over the previous year. Body weight and height were each measured twice and averaged for analysis. Body mass index was calculated as weight in kilograms divided by height in meters squared. Waist circumference was measured at the uppermost lateral border of the iliac crest with a Gulick II tape and repeated until 2 measures agreed within 1 cm. Three seated blood pressure measurements were obtained by trained and certified staff members after ≥5 minutes of quiet rest. These measurements were performed according to a standard protocol using an aneroid sphygmomanometer. The measurements were averaged as baseline systolic and diastolic blood pressure for analysis. Pulse pressure was calculated by subtracting diastolic blood pressure from systolic blood pressure. Hypertension was defined as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg and/or current use of antihypertensive medication.
Ankle brachial index was obtained per standard protocol. After the participant rested supine for 5 minutes, systolic blood pressure was measured in both arms with an appropriately sized arm cuff. For each leg, systolic blood pressure in each posterior tibial and dorsalis pedis artery was measured. All pressures were detected with a continuous-wave Doppler ultrasound probe. The leg-specific ankle brachial index was calculated by dividing the higher systolic blood pressure in the posterior tibial or dorsalis pedis artery by the higher of the right or left brachial systolic blood pressure. PAD was defined as an ankle brachial index <0.9 or a history of arm or leg revascularization in the present analysis.
Blood glucose, cholesterol, triglycerides, glycosylated hemoglobin (HbA 1c ), phosphate, calcium, alkaline phosphatase, total parathyroid hormone, uric acid, hemoglobin, albumin, bicarbonate, and white blood cell (WBC) counts were measured using standard laboratory methods. Serum high-sensitivity C-reactive protein (hsCRP), homocysteine, and cystatin C were measured using a particle-enhanced immunonephelometric method. Fibrinogen was measured using an immunochemical reaction method. Serum myeloperoxidase was measured using a chemiluminescent microparticle immunoassay (Architect ci8200; Abbott Diagnostics, Abbott Park, Illinois). Urinary albumin was measured by radioimmunoassay. Diabetes was defined as fasting glucose ≥126 mg/dl, random glucose ≥200 mg/dl, and/or the use of insulin or other antidiabetic medication. The eGFR was calculated using the reexpressed Modification of Diet in Renal Disease (MDRD) equation after calibrating serum creatinine measurements to isotope dilution mass spectrometry–traceable values. A homeostasis model assessment (HOMA) was calculated to evaluate insulin resistance using the following formula: fasting serum insulin (μU/ml) × fasting plasma glucose (mmol/L)/22.5. Albumin-corrected calcium levels were calculated as follows: serum calcium (mg/dl) + [0.8 × (4 − serum albumin (g/dl)]. All laboratory measurements were performed at the CRIC Central Clinical Laboratory at the University of Pennsylvania.
Baseline characteristics of participants are expressed as mean ± SD for continuous variables and as percentages for categorical variables by PAD status. Statistical significance was tested using analysis of variance for continuous variables and chi-square tests for categorical variables. Multiple logistic regression analyses were used to explore the associations of PAD with traditional and novel cardiovascular disease risk factors. In the multiple traditional risk factor model, all demographic variables and traditional risk factors that were significant in univariate analysis, including age, gender, race or ethnicity, cigarette smoking, history of hypertension and diabetes, physical activity (total METs/week), high-density lipoprotein cholesterol, pulse pressure, eGFR, and 13 CRIC clinic sites were simultaneously included. In the multivariate analysis of novel cardiovascular disease risk factors, 2 sets of covariates were adjusted. First, demographic variables, including age, gender, race, and CRIC clinical sites, were adjusted. Then, traditional cardiovascular disease risk factors, including cigarette smoking, physical activity, diabetes, hypertension, high-density lipoprotein cholesterol, pulse pressure, and eGFR, were included. Odds ratios and 95% confidence intervals (CIs) of PAD associated with categorical variables or 1-SD increases in continuous variables were presented. For those novel risk factors that were not normally distributed, including HOMA of insulin resistance, hsCRP, total parathyroid hormone, and myeloperoxidase, the log transformation was taken. All analyses were conducted using SAS version 9.1 (SAS Institute Inc., Cary, North Carolina). All p values were 2 sided, and statistical significance was defined as p <0.05.
Results
Characteristics of CRIC participants by PAD status are listed in Table 1 .
| Variable | PAD | p Value | |
|---|---|---|---|
| Yes (n = 754) | No (n = 3,004) | ||
| Age (years) | 62.2 ± 9.1 | 57.2 ± 11.2 | <0.0001 |
| Men | 53.4% | 54.6% | 0.58 |
| Race/ethnicity | <0.0001 | ||
| White | 39.5% | 48.0% | |
| Black | 50.1% | 40.4% | |
| Other | 10.3% | 11.6% | |
| High school education | 71.4% | 81.8% | <0.0001 |
| Physical activity (METs/week) | 165.07 ± 119.8 | 208.04 ± 150.7 | <0.0001 |
| Cigarette smoking | <0.0001 | ||
| Never smoker | 29.6% | 48.6% | |
| Former smoker | 51.1% | 39.6% | |
| Current smoker | 19.4% | 11.8% | |
| Alcohol drinker | 14.6% | 22.1% | <0.0001 |
| Hypertension | 93.2% | 84.1% | <0.0001 |
| Diabetes mellitus | 64.6% | 42.3% | <0.0001 |
| Body mass index (kg/m 2 ) | 32.9 ± 8.6 | 31.8 ± 7.6 | 0.0003 |
| Waist circumference (cm) | 108.3 ± 17.9 | 105.0 ± 17.4 | <0.0001 |
| Pulse pressure (mm Hg) | 65.1 ± 19.8 | 54.5 ± 18.3 | <0.0001 |
| High-density lipoprotein cholesterol (mg/dl) | 45.3 ± 14.3 | 48.2 ± 15.8 | <0.0001 |
| Low-density lipoprotein cholesterol (mg/dl) | 99.7 ± 34.8 | 103.9 ± 35.3 | 0.004 |
| Calcium (mg/dl) | 9.16 ± 0.56 | 9.20 ± 0.50 | 0.07 |
| Phosphate (mg/dl) | 3.83 ± 0.71 | 3.68 ± 0.65 | <0.0001 |
| Calcium phosphate product | 35.0 ± 6.64 | 33.9 ± 6.21 | <0.0001 |
| Alkaline phosphatase (U/L) | 96.7 ± 36.4 | 90.5 ± 34.3 | <0.0001 |
| Log [total parathyroid hormone (pg/ml)] | 4.2 ± 0.75 | 4.0 ± 0.69 | <0.0001 |
| Log [hsCRP (mg/L)] | 1.20 ± 1.26 | 0.87 ± 1.26 | <0.0001 |
| WBC count (×1,000/μl) | 7.08 ± 3.12 | 6.45 ± 1.97 | <0.0001 |
| Homocysteine (μmol/L) | 16.6 ± 6.43 | 14.6 ± 5.94 | <0.0001 |
| Fibrinogen (mg/dl) | 4.52 ± 1.29 | 4.05 ± 1.16 | <0.0001 |
| Uric acid (mg/dl) | 7.82 ± 1.98 | 7.29 ± 1.89 | <0.0001 |
| Log [myeloperoxidase (pmol/L)] | 4.82 ± 0.97 | 4.65 ± 1.18 | 0.0004 |
| Serum albumin (g/dl) | 3.87 ± 0.48 | 3.97 ± 0.46 | <0.0001 |
| Hemoglobin (g/dl) | 12.2 ± 1.71 | 12.8 ± 1.77 | <0.0001 |
| HbA 1c (%) | 7.07 ± 1.53 | 6.51 ± 1.53 | <0.0001 |
| HOMA of insulin resistance | 8.1 ± 9.7 | 6.2 ± 8.1 | <0.0001 |
| eGFR (ml/min/1.73 m2) | 38.6 ± 12.2 | 44.1 ± 13.6 | <0.0001 |
| Cystatin C (mg/L) | 1.74 ± 0.56 | 1.45 ± 0.52 | <0.0001 |
| Urinary albumin (g/24 hours) | 0.91 ± 2.14 | 0.6 ± 1.43 | <0.001 |
The age-, gender-, race-, and clinic-adjusted prevalence of PAD according to eGFR levels by diabetes status is displayed in Figure 1 . There was a significant, graded, and inverse association between eGFR and the prevalence of PAD in patients with and without diabetes (p for trend <0.0001 for the 2 groups). The prevalence of PAD was also significantly higher in patients with diabetes compared to those without, stratified by eGFR categories (p for group difference <0.0001).
The odds ratios and 95% CIs of PAD associated with traditional risk factors from a multivariate model are listed in Table 2 . Age, black race, former and current cigarette smoking, history of diabetes, and pulse pressure were positively and significantly associated with the odds of PAD, while physical activity, high-density lipoprotein cholesterol, and eGFR were inversely and significantly associated with the odds of PAD.
| Variable | Odds Ratio (95% CI) ⁎ | p Value |
|---|---|---|
| Age (5 years) | 1.19 (1.13–1.25) | <0.0001 |
| Male gender | 0.83 (0.69–1.01) | 0.065 |
| Race/ethnicity | 0.098 | |
| White | 1.00 (reference) | |
| Black | 1.24 (1.02–1.51) | |
| Other | 1.01 (0.68–1.49) | |
| Cigarette smoker | <0.0001 | |
| Never smoker | 1.00 (reference) | |
| Former smoker | 1.78 (1.46–2.17) | |
| Current smoker | 2.68 (2.06–3.49) | |
| Diabetes mellitus | 1.75 (1.45–2.12) | <0.0001 |
| Hypertension | 1.15 (0.83–1.60) | 0.41 |
| Physical activity (146 METs/week † ) | 0.87 (0.78–0.97) | 0.01 |
| High-density lipoprotein cholesterol (16 mg/dl † ) | 0.83 (0.74–0.91) | 0.0003 |
| Pulse pressure (19 mm Hg † ) | 1.30 (1.18–1.43) | <0.0001 |
| eGFR (14 ml/min per 1.73 m 2 † ) | 0.74 (0.67–0.82) | <0.0001 |
⁎ All covariates listed in the table were adjusted simultaneously in addition to 13 CRIC clinic sites.
The age-, gender-, race-, and clinic-adjusted and multivariate-adjusted odds ratios of PAD associated with novel risk factors are listed in Table 3 . After adjusting for demographic variables, higher levels of phosphate, calcium phosphate product, alkaline phosphatase, log-transformed total parathyroid hormone, log-transformed hsCRP, WBC count, homocysteine, fibrinogen, uric acid, log-transformed myeloperoxidase, HbA 1c , log-transformed HOMA of insulin resistance, cystatin C, and urine albuminuria were significantly associated with higher odds of PAD, while higher levels of albumin and hemoglobin were significantly associated with lower odds of PAD. After further adjusting for multiple traditional cardiovascular disease risk factors, hsCRP, WBC count, fibrinogen, uric acid, HbA 1c , HOMA of insulin resistance, and cystatin C remained significantly associated with higher odds of PAD.
